This is the official code for Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021). Besides, we provide highly modularized explainers for Graph Classification Tasks. Some of them are adapted from the image domain. Below is a summary:
| Explainer | Paper |
|---|---|
| ReFine | Towards Multi-Grained Explainability for Graph Neural Networks |
| SA | Explainability Techniques for Graph Convolutional Networks. |
| Grad-CAM | Explainability Methods for Graph Convolutional Neural Networks. |
| DeepLIFT | Learning Important Features Through Propagating Activation Differences |
| Integrated Gradients | Axiomatic Attribution for Deep Networks |
| GNNExplainer | GNNExplainer: Generating Explanations for Graph Neural Networks |
| PGExapliner | Parameterized Explainer for Graph Neural Network |
| PGM-Exapliner | PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks |
| Screener | Causal Screening to Interpret Graph Neural Networks |
| CXPlain | Cxplain: Causal Explanations for Model Interpretation under Uncertainty |
Requirements
- CPU or NVIDIA GPU, Linux, Python 3.7
- PyTorch >= 1.5.0, other packages
- Pytorch Geometric. Official Download.
# We use TORCH version 1.6.0
CUDA=cu102
TORCH=1.6.0
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric==1.7.0
-
Visual Genome (optional). Google Drive Download. This is used for preprocessing the VG-5 dataset and visualizing the generated explanations. Manually download it to the same directory as
data. (This package can be accessed by API, but we found it slow to use.) You can still run the other datasets without downloading it. -
Other packages
pip install tqdm logging pathlib matplotlib argparse json pgmpy==0.1.11
# For visualization (optional)
conda install -c conda-forge rdkit
- The processed raw data for
BA-3motifis available in thedata/folder. - Datasets
MNIST,Mutagenicitywill be automatically downloaded when training models. - We select and label 4443 graphs from https://visualgenome.org/ to construct the VG-5 dataset. The graphs are labeled with five classes: stadium, street, farm, surfing, forest. Each graph contains regions of the objects as the nodes, while edges indicate the relationships between object nodes. Download the dataset from Google Drive. Arrange the dir as
data ---BA3
|------VG
|---raw
Please also cite Visual Genome (bibtex) if you use this dataset.
We provide the trained GNNs in param/gnns for reproducing the results in our paper. To retrain the GNNs, run
cd gnns/
bash run.shThe trained GNNs will be saved in param/gnns.
- For global training of PGExplainer and ReFine, run
cd train/
bash run.sh- Load datasets
from utils.dataset import get_datasets
from torch_geometric.data import DataLoader
name = 'ba3'
train_dataset, val_dataset, test_dataset = get_datasets(name=name)
test_loader = DataLoader(test_dataset, batch_size=1)- Instantiate the explainer
from explainers import *
device = torch.device("cuda")
gnn_path = f'param/gnns/{name}_net.pt'
refine = torch.load(f'param/refine/{name}.pt') # load pretrained
refine.remap_device(device)- Explain
for g in test_loadder:
refine.explain_graph(g, fine_tune=True,
ratio=0.4, lr=1e-4, epoch=20)For baseline explainers, e.g.,
gnn_explainer = GNNExplainer(device, gnn_path)
gnn_explainer.explain_graph(g,
epochs=100, lr=1e-2)
screener = Screener(device, gnn_path)
screener.explain_graph(g) - Evaluation & Visualization
Evaluation and visualization are made universal for every explainer. After explaining a single graph, the pair (graph, edge_imp:np.ndarray) is saved as explainer.last_result by default, which is then evaluated or visualized.
ratios = [0.1 *i for i in range(1,11)]
acc_auc = refine.evaluate_acc(ratios).mean()
racall = refine.evaluate_recall(topk=5)
refine.visualize(vis_ratio=0.3) # visualize the explanationTo evaluate ReFine-FT and ReFine in the testing datasets, run
python evaluate.py --dataset ba3The results will be included in file results/ba3_results.json, where ReFine-FT.ACC-AUC (ReFine-FT.Recall@5) and ReFine.ACC-AUC (ReFine.Recall@5) are the performances of ReFine-FT and ReFine, respectively.
Please cite our paper if you find the repository useful.
@inproceedings{refine,
title={Towards Multi-Grained Explainability for Graph Neural Networks},
author={Wang, Xiang and Wu, Ying-Xin and Zhang, An and He, Xiangnan and Chua, Tat-Seng},
booktitle={Proceedings of the 35th Conference on Neural Information Processing Systems},
year={2021}
}
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